Abstract
Nonlinear regression is widely used to fit experimental data to a specific mathematical model to extract numerical values for parameters representing the studied process. However, assessing the degree of precision that can be expected from such an analysis for a given parameter can be quite challenging for complex mathematical models. To address this issue, we propose here a method based on the analysis of a large number of data sets generated in such a way to mimic specific experimental conditions. Applying this methodology to high-affinity binding models, we report here a quantitative analysis of the robustness of such models, and how the precision on the fitting parameters can be expected to vary based on, e.g., the initial experimental conditions, the number or distribution of experimental points, or the experimental variability. We also show that these models, although widely used, are intrinsically limited by the fact that the two main fitting parameters, one representing the concentration of the studied species and the other its affinity, are inversely correlated, but demonstrate that this limitation can be overcome by global analysis of multiple data series generated at different concentrations of the titrated species.
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24 June 2021
In Para 7 under the section "Results", the term Sect. 2, Sect. 3 and Sect. 1 was incorrect. Now, they have been corrected to section 2, section 3 and section 1
References
Karlsson R (1994) Real-time competitive kinetic analysis of interactions between low-molecular-weight ligands in solution and surface-immobilized receptors. Anal Biochem 221:142–151
Kenniston JA, Faucette RR, Martik D, Comeau SR, Lindberg AP, Kopacz KJ, Conley GP, Chen J, Viswanathan M, Kastrapeli N, Cosic J, Mason S, DiLeo M, Abendroth J, Kuzmic P, Ladner RC, Edwards TE, TenHoor C, Adelman BA, Nixon AE, Sexton DJ (2014) Inhibition of plasma kallikrein by a highly specific active site blocking antibody. J Biol Chem 289:23596–23608
Lima e Silva R, Kanan Y, Mirando AC, Kim J, Shmueli RB, Lorenc VE, Fortmann SD, Sciamanna J, Pandey NB, Green JJ, Popel AS, Campochiaro PA (2017) Tyrosine kinase blocking collagen IV—derived peptide suppresses ocular neovascularization and vascular leakage. Sci Transl Med 9(373):aai8030. https://doi.org/10.1126/scitranslmed.aai8030
Nieba L, Krebber A, Plückthun A (1996) Competition BIAcore for measuring true affinities: large differences from values determined from binding kinetics. Anal Biochem 234:155–165
Tamaskovic R, Simon M, Stefan N, Schwill M, Plückthun A (2012) Designed ankyrin repeat proteins (DARPins): from research to therapy. Methods Enzymol 503:101–134
Teufel DP, Bennett G, Harrison H, van Rietschoten K, Pavan S, Stace C, Le Floch F, Van Bergen T, Vermassen E, Barbeaux P, Hu TT, Feyen JHM, Vanhove M (2018) Stable and long-lasting, novel bicyclic peptide plasma kallikrein inhibitors for the treatment of diabetic macular edema. J Med Chem 6:2823–2836
Vanhove E, Vanhove M (2018) Affinity determination of biomolecules: a kinetic model for the analysis of pre-equilibrium titration curves. Eur Biophys J 47(8):961–966
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Vanhove, M. A method to assess the robustness of complex mathematical models used for quantitative interpretation of experimental data by nonlinear regression analysis: application to high-affinity binding models. Eur Biophys J 50, 1045–1054 (2021). https://doi.org/10.1007/s00249-021-01556-y
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DOI: https://doi.org/10.1007/s00249-021-01556-y